| Course Unit Code | 156-0380/01 |
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| Number of ECTS Credits Allocated | 5 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | First Year |
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| Semester when the Course Unit is delivered | Summer Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| FIL03 | Ing. Lenka Johnson Filipová, Ph.D. |
| BAL112 | doc. Ing. Jiří Balcar, Ph.D., MBA |
| PYT005 | doc. Ing. Mariola Pytliková, Ph.D. |
| JAW127 | doc. Ing. Jan Janků, Ph.D. |
| BAD0012 | Ing. Ondřej Badura, Ph.D. |
| AND0096 | doc. Antonio Rodríguez Andrés, Ph.D. |
| BRI0043 | doc. Ing. Zuzana Schwidrowski, Ph.D. |
| Summary |
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| Learning Outcomes of the Course Unit |
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| The objective of this course is to apply econometric methods to real world problems. The emphasis is on perspective of professional users of econometrics and illustrate how empirical researchers think about and apply econometrics methods. The aim is to equip students with broad and rigorous tools that would allow them to (i) conduct an independent econometric analysis of problems they may encounter in their work and (ii) where suitable, draw operational and/or policy recommendations. |
| Course Contents |
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1. Introduction to economics and Stata, and its use for descriptive statistics.
2. Least squares method, linear regression and OLS estimator properties.
3. Credibility of estimation, hypothesis testing, measurement errors and feedback in the presence of stochastic variables.
4. Interpretation and comparison of models (including model selection criteria).
5. Basics of forecasting and simulation.
6. Heteroskedasticity and autocorrelation.
7. Principles of time series analysis and volatility (conditional and variance modeling).
8. Endogenity, estimation using instrumental variables.
9. Logit and probit models.
10. Multinomial models and models of ordered answers.
11. Count data (Poisson regression model, negative binomial model, general count regression), “duration” data.
12. Tobit models (censored variables), treatment effects.
13. Linear models of panel data: fixed and random effects.
14. Linear models of panel data: static and dynamic models, incomplete panels (/attrition), tests of non-stationarity and cointegration. |
| Recommended or Required Reading |
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| Required Reading: |
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| Wooldridge, J. M. (2016), Introductory Econometrics: A Modern Approach (6th edition), Cengage Learning, Inc. |
| Wooldridge, J. M. (2016), Introductory Econometrics: A Modern Approach (6th edition), Cengage Learning, Inc. |
| Recommended Reading: |
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Acock, A. C. (2018), A Gentle Introduction to Stata, 6th edition, A Stata Press Publication.
Heiss, F. (2016), Using R for Introductory Econometrics, 1st edition. This textbook is compatible with "Introductory Econometrics" by J. M. Wooldridge in terms of topics, organization, terminology and notation.
Verbeek, M. (2017), A Guide to Modern Econometrics, Wiley Publisher.
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Acock, A. C. (2018), A Gentle Introduction to Stata, 6th edition, A Stata Press Publication.
Heiss, F. (2016), Using R for Introductory Econometrics, 1st edition. This textbook is compatible with "Introductory Econometrics" by J. M. Wooldridge in terms of topics, organization, terminology and notation.
Verbeek, M. (2017), A Guide to Modern Econometrics, Wiley Publisher.
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| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit | Credit | 100 (100) | 51 |
| Písemka | Written test | 100 | 51 |